There are certain difficulties and unpleasant issues related to conventional diagnostic tools. These factors tilted the researchers toward finding an alternative non-invasive way of diagnosis. This alternate approach usually involves physiological and lifestyle-related data. The non-invasive tools are more convenient for common people as they are user-friendly and have no side effects. At the same time, they are cost-effective as well. The non-invasive diagnosis is also preferred by the people who live in places where medical facilities are not abundant. This study concentrates on detecting a person as hypertensive by analyzing certain parameters in speech using machine learning approaches. We identify some phonemes and features of speech that are more sensitive to capture the distortions in speech due to hypertension. Four different machine learning methods involving both classical and state-of-the-art methods in our study show the effectiveness of both types of machine learning methods in different dimensions. The study shows inspiring results in terms of prediction accuracy ([Formula: see text]95%) as well as identifying a minimal set of hypertension-sensitive features. It is also found that when we combine the predictions of both classical and state-of-the-art methods, the result gives more reliable predictions.

Recently, social media platforms have been widely utilized as information sources due to their effortless accessibility and reduced costs. However, online platforms like Instagram, Twitter and Facebook get influenced by their users via fake news/reviews. The main intention of spreading fake news is to mislead other network users, which highly affects businesses, political parties, etc. Thus, an effective methodology is needed to predict fake news from social media automatically. The major objective of this proposed study is to identify and classify the given Twitter input data as real or fake through deep learning mechanisms. The proposed study involves four stages: pre-processing, embedded word analysis, feature extraction, and fake news/reviews prediction. Initially, pre-processing is performed to enhance the quality of data with the help of tokenization, stemming and stop word removal. Embedded word analysis is done using Advanced Word2Vec and GloVe modeling to enhance the performance of a proposed prediction model. Then, the hybrid deep learning model named Dense Convolutional assisted Gannet Optimal Bi-directional Network (DC_GO_BiNet) is introduced for feature extraction and prediction. A Dense Convolutional Neural Network (DCNN) is hybridized with a bi-directional long-short-term memory (Bi-LSTM) model to extract the essential features and predict fake news from the given input text. Also, the proposed model’s parameters are fine-tuned by adopting a gannet optimization (GO) algorithm. The proposed study used three different datasets and obtained higher classification accuracy as 99.5% in Fake News Detection on Twitter EDA, 99.59% in FakeNewsNet and 99.51% in ISOT. The analysis proves that the proposed model attains higher prediction results for each dataset than others.

Science automation, driven by advanced technologies, such as artificial intelligence (AI), is reshaping research in a variety of disciplines. The exponential growth of publications increases the need for automation in systematic literature reviews (SLRs). In this study, we investigated how AI applications support SLRs at different stages. After evaluating twenty-two AI applications, we identified the most useful based on their functionalities in the SLR process. This study provides important insights into the dynamic interplay between science automation, AI, and a systematic literature review. The main contribution is the identification and discussion of the relevance of the analyzed applications at different stages of the systematic literature review. We also discuss the potential for the automation and augmentation of research activities to address various scientific problems.

Human skeleton-based posture anomaly detection has been widely applied in the field of physical education teaching. The existing spatio-temporal graph convolutional networks (ST-GCN) can fully utilize the local and global information of the human skeleton for action recognition, but the entire model requires a large amount of computation and the modeling of high-order relationships between joint points of the human skeleton is insufficient. To this end, this paper proposes a novel domain adaptive hypergraph convolutional network for basketball posture anomaly analysis by exploiting 2D skeleton information. First, we designed an effective hypergraph convolution feature extraction network to improve the high-order dependency modeling. To further improve the performance of the hypergraph convolutional network, we introduce domain adaptive learning technology to supervise the feature extraction learning of the target domain (2D skeleton) through the source domain (3D skeleton). At last, we construct a basketball action teaching analysis dataset for model evaluation. We conducted a large number of comparative experiments on the public dataset NTU RGB+D and our self-built dataset. All the results showed that our proposed hypergraph convolutional model effectively extracts features of 2D human skeletons, and by introducing domain adaptive learning, the performance of basketball anomaly detection is further improved.

In today’s information age, intelligent manufacturing has become a significant trend of future industrial development. The neural network plays a vital role as the basis of smart manufacturing. This is a kind of intelligent algorithm that can simulate human brain organization, with strong learning ability and computing power, and has achieved remarkable results in the application field; the most typical representative is deep neural network, especially in recent years, an artificial intelligence developed based on deep neural network has entered the application stage, providing a new idea and model for industrial production. Based on this, taking the clothing production process as an example, this paper combined transfer learning and network fine-tuning technology to build a Faster RCNN model based on the ResNet-101 network to realize the positioning and recognition of clothing style map features and create an intelligent generation platform for clothing process flow on this basis.

The path planning of mobile robots helps robots to perceive environment using the information obtained from sensors and plan a route to reach the target. With the increasing difficulty of task, the environment the mobile robots face becomes more and more complex. Traditional path planning methods can no longer meet the requirements of mobile robot navigation in complex environment. Deep reinforcement learning (DRL) is introduced into robot navigation However, it may be time-consuming to train DRL model when the environment is very complex and the existing environment may differ from the unknown environment. In order to handle the robot navigation in heterogeneous environment, this paper utilizes deep transfer reinforcement learning (DTRL) for mobile robot path planning. Compared with DRL, DTRL does not require the distribution of the existing environment is the same as that of the unknown environment. Additionally, DTRL can transfer the knowledge of existing model to new scenario to reduce the training time. The simulations show that DTRL can reach higher success rate than DRL for heterogeneous environment robot navigation. By using local policy, it costs less time to train DTRL than DRL for a complex environment and DTRL can consume less navigation time.

Rolling bearings play an important role in rotating machinery. According to statistics, rolling bearings cause one-third faults of rotating machinery. Once a rolling bearing malfunctions, it may induce maintenance, affect work efficiency, or even cause the entire equipment to malfunction. Therefore, accurately determining the operating status of bearings is of great significance for maintaining the health of the rotating machinery. Most current fault detections of rolling bearing works focus on traditional anomaly detection models which assume the training set to follow the same distribution of test set. This assumption does not hold in fault detection of rolling bearings across different conditions and traditional anomaly detection models may be invalid. This paper introduces domain adaptation anomaly detection (DAAD) in the fault detection of rolling bearings to address this issue. DAAD can adapt anomaly detection across different distributions. The experiments of rolling bearing fault detection under single condition or across different condition show that DAAD is superior to most of the traditional anomaly detection models.

Music emotion recognition plays an important role in many applications such as music material library construction and music recommendation system. The current music emotion recognition mainly focuses on discrete emotions or continuous emotions under single scene. However, on the one hand, intensity is one of important aspects of emotion, which can be represented as emotion rank or ordinal class. On the other hand, there may be not enough music emotion data in training set, which needs to transfer music emotion recognition model learnt from the music data in a source domain. The distribution of existing music data in target domain may differ from target music dataset. In order to overcome these two issues, this paper proposes to utilize transfer ordinal label learning (TOLL) to estimate music emotion. Compared with the previous works, TOLL-based music emotion intensity estimation implements music intensity estimation through transferring the knowledge in the existing source domain to unknown target domain. The experiments on several datasets show that TOLL can achieve promising results for emotion intensity estimation in single scene or across different scenes.

As an important reflection of the national economy system, stock market is closely related to the development of a country, which has received widespread attention of researchers in the economics. With the daily trading of the stock market, stock price forecasting has gradually been one of the common concerns in the economic analysis. Compared with traditional forecasting task, the stock price is interval data which can be handled by interval data regression or multi-output regression. Previous stock forecasting merely considers the stock price in homogeneous scenarios. However, the price distributions from different stocks may be heterogeneous. It is a challenging task to analyze the relationship between different stocks which follow heterogeneous distributions. In order to forecast stocks in heterogeneous scenarios, this paper introduces multi-output transfer learning into stock price forecasting. Compared with traditional regression or multi-output regression models, the multi-output transfer regression can predict opening price, closing price, highest price and lowest price of stocks and utilize source domain of a known stock to enhance the prediction of target stock price which may have limited known data in training set. The experimental results on four public market indices demonstrate the effectiveness of multi-output transfer regression for stock price forecasting.